A Two-Stage Approach for the Remaining Useful Life Prediction of Bearings using Deep Neural Networks

IEEE Transactions on Industrial Informatics(2019)

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摘要
The degradation of bearings plays a key role in the failures of industrial machinery. Prognosis of bearings is critical in adopting an optimal maintenance strategy to reduce the overall cost and to avoid unwanted downtime or even casualties by estimating the remaining useful life (RUL) of the bearings. Traditional data-driven approaches of RUL prediction rely heavily on manual feature extraction a...
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关键词
Feature extraction,Degradation,Neural networks,Training,Hidden Markov models,Prognostics and health management,Noise reduction
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